Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
This work addresses the challenge of learning representations from unlabeled time-series data, which is important for domains like healthcare or finance, but it appears incremental as it builds on existing contrastive learning frameworks by incorporating spectral information.
The paper tackles the problem of unsupervised time-series representation learning by proposing a Bilinear Temporal-Spectral Fusion (BTSF) framework that uses instance-level augmentation and iterative fusion of temporal and spectral information, resulting in consistent and significant outperformance over state-of-the-art methods in classification, forecasting, and anomaly detection tasks.
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations. Existing works mainly adopt the framework of contrastive learning with the time-based augmentation techniques to sample positives and negatives for contrastive training. Nevertheless, they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context. Besides, they all pay no attention to incorporate the spectral information in feature representation. In this paper, we propose a unified framework, namely Bilinear Temporal-Spectral Fusion (BTSF). Specifically, we firstly utilize the instance-level augmentation with a simple dropout on the entire time series for maximally capturing long-term dependencies. We devise a novel iterative bilinear temporal-spectral fusion to explicitly encode the affinities of abundant time-frequency pairs, and iteratively refines representations in a fusion-and-squeeze manner with Spectrum-to-Time (S2T) and Time-to-Spectrum (T2S) Aggregation modules. We firstly conducts downstream evaluations on three major tasks for time series including classification, forecasting and anomaly detection. Experimental results shows that our BTSF consistently significantly outperforms the state-of-the-art methods.